Insights and pitfalls of artificial neural network modeling of competitive multi-metallic adsorption data

Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects

“…The adsorption performance of different adsorbents for the removal of Cr(III), Cr(IV), Cu(II), Pb(II), As(III), Zn(II), Cd(II), and Hg(II) by different adsorbents was determined by the using various AI tools, mainly ANN [53][54][55][56][57][58][59][60][61] Some studies also employed the AI tools to assess the performance of adsorption for the simultaneous removal of multiple metals from aqueous phase [62] 13]. Studies also evaluated the performance of various adsorbents for the removal of dyes in a continuous system using AI tools [63].…”

Modelling of Lead and Cadmium Removal by Porous Biosorbent in Silent and Ultrasound-assisted Systems

“…Two parameters are considered to characterize biosorption dynamics: the diffusion coe cient ({D}_{ef}) and the adsorption rate constant ({k}_{a}). To determine their values, experimental obtained data according to procedure detailed in subchapter 2.2.4 and the dynamic model equations (5)(6)(7)(8)(9)(10)(11) were used.…”

“…However, effective and economically feasible removal is still a challenge due to direct or indirect interaction between many in uencing factors 8,9 among which: a) co-ion presence and properties that may cause synergic, antagonistic and/or non-interaction effects, b) adsorbent physicochemical characteristics (particle and pore size, area, surface chemistry) and properties (easy separation, good reusability, high adsorptivity), and c) medium conditions (pH, temperature, concentration, ionic strength, etc.). In the same time, as clearly describes Di Natale et al, 2009 10 the adsorption processes must be addressed from two perspectives: solute-solvent-sorbent interactions (thermodynamic approach) and diffusive-convective mass transfer of solute within porous sorbents.…”

Ternary Multicomponent Adsorption Modelling Using ANN, LS-SVR, and SVR Approach – Case Study

“…9,10 ANNs are applied successfully to model the non-linear behaviour between dependent and independent variables without knowing any previous details about the physical process in complex systems. 7,[11][12][13][14] However, to the best of our knowledge, very few studies are devoted to the application of LS-SVM or SVM approach to model the competitive adsorption of heavy metals. 15,16 Therefore, the major motivation behind this study was to assess the predictability power of three modelling approaches {ANN, SVM, and LS-SVM} in modelling the nonlinear relationships between the removal capacity from aqueous solution of five ternary heavy metal systems on different adsorbents and the independent parameters.…”